Feature Prioritization with RICE
A Product Manager’s ideal world would have unlimited time and resources to create the perfect product. But let's face it, the reality is that our engineering teams face excessive workloads, our customers are requesting new features, critical bugs are hampering the user experience and our stakeholders have a list of suggestions they would want implemented urgently. With this pressure of urgency and a growing list of features to build, it is important for us to pause and question:
Why do we need to build this feature? Why do we need to build it now?
Great product managers are able to pose these questions and understand that the features need to be tackled in a structured manner with some priorities assigned to them. One of our most crucial responsibilities is deciding which features to prioritize and implement in our product.
What is Feature Prioritization?
Feature prioritization is the process of determining which features to build first, based on a set criteria. This decision-making process is critical for a product's success, as it helps allocate the limited resources effectively, ensures that customer needs are met, and aligns development efforts with strategic business goals.
Prioritization is not about saying yes, but about saying no to the less important features so you can say yes to the most important ones.
(Co-author of "Lean Analytics")
Consider, for instance, a choice faced on an e-commerce website: adding detailed product descriptions to individual product pages or altering the color of the obscured "add to cart" button to make it more prominent. What feature should be prioritized given that resources limit us from working on both these simultaneously?
Fig 1. Product feature choices: product descriptions for each product (left) and button redesign to stand out more (right)
You may instinctively consider the team’s bandwidth and see what can be built faster, prioritizing the speed of delivering a solution to the customer. Changing the color of a button will take much less time than gathering product descriptions for all the products on the website. That's one way to prioritize, but is this truly aligned with the customer's needs?
Maybe the descriptions are needed first as customers can't even decide what to purchase without their descriptions. In this case, add-to-cart becomes a secondary concern.
To determine the right course of action, one must delve deeper and ascertain what need is more urgent for the customer. Additionally, the number of customers each problem is impacting may also not be the same for both features so you may also consider thinking about the scale of the problem. This seems like a lot of factors to consider so how do we tackle this?
Introducing the RICE Framework
The RICE framework is a widely used method for feature prioritization that takes into account four key factors: Reach, Impact, Confidence, and Effort. RICE provides a systematic approach to assess and compare different features and will combine all our earlier thoughts into a quantifiable prioritization rating. Let's unpack these four factors:
Reach (R): Reach quantifies how many users will be affected by the feature. It is usually measured in terms of the number of users, customers, or audience segments impacted by the feature. A higher reach indicates a larger user base that will benefit from the feature.
Impact (I): Impact represents the potential influence or value the feature will bring to the product. It could be measured in terms of increased revenue, improved user satisfaction, or any other relevant key performance indicators (KPIs).
Confidence (C): Confidence gauges the level of certainty in the estimated impact. It reflects the confidence in the data and assumptions used to calculate impact. High confidence indicates that the impact estimation is reliable.
Effort (E): Effort measures the resources, time, and complexity required to develop the feature. It is often measured in person-weeks or other time-based units. A lower effort suggests that the feature can be delivered more quickly and efficiently.
Applying the following formula to the problem results in numeric values that are a direct representation of the prioritization sequence: a bigger value representing the feature that takes higher prioritization and vice versa. Enough with the theory, let's put this into practice!
Fig 2. The formula of the RICE Framework for feature prioritization
Applying the RICE Framework
Building on our previous example, imagine you are a product manager for an e-commerce platform, and you have two feature ideas to prioritize:
A) Adding product descriptions for all products, and
B) Redesigning the add-to-cart button.
Feature A (product descriptions): It would target users who click on products for details, so the reach is 65%.
Feature B (button redesign): It would target all users browsing products, so the reach is 100%.
Feature A: The product descriptions have the potential to increase purchases by 20%, resulting in a significant impact.
Feature B: The button redesign is expected to improve clicks on add-to-cart, increasing the purchases by 10%, and making it moderately impactful.
Feature A: You have high confidence in the estimate, as similar approaches have succeeded in the past so you rate it 8 on a 10 scale.
Feature B: You have moderate confidence, as the redesign's impact is based on user preferences but lacks concrete data so you rate it 5.
Feature A: Curating and integrating all the product descriptions is estimated to take 4 person-weeks.
Feature B: The button redesign is a smaller project and would take 1 person-weeks.
Now, let's calculate the RICE scores:
Feature A RICE = (65 * 20 * 8) / 4 = 2600
Feature B RICE = (100 * 10 * 5) / 1 = 5000 - Winner!
Based on the RICE scores, you would prioritize Feature B (button redesign) over Feature A (product descriptions).
Is RICE the only way?
While the RICE framework is powerful, it's not the only method for feature prioritization. Other frameworks, such as MoSCoW, Kano Model, and Value vs. Complexity can also be useful, depending on your specific needs and context.
It is important to also be wary of the RICE method’s limitations. It relies on subjective estimates for reach, impact, and confidence, which can introduce biases and inaccuracies. It also lacks any weightage system, treating all four factors (R, I, C, E) equally, which may not be appropriate for every situation. Some features may require a different weighting of factors.
Priority is not a label, but a direction.
Jason Goldberg — Founder of Jobster
Lastly, remember that priorities are dynamic and can change with changing circumstances so don't fixate yourself on the numeric values too much. A sudden change in the tech landscape, such as the introduction of advanced GPT models, or an organizational strategy shift, such as a focus on growth over profitability, may change the assumptions with which you calculated your priority scores. Be responsive to the market needs and revise product strategy to respond to the evolving needs of the market.